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A supervised false negative (FN), in machine learning classification task , is an example of a machine-learning task. This type of problem requires error that occurs when a model be trained on incorrectly classes a labeled datasetsample as negative even though it is actually positive. Each pair consists of an input object A false negative (such an imageFN) as well as is when a label (such the object's classification). The model is then used to predict the label of unknown input objectsfails identify a positive sample.

==The Iris Dataset==The [[Iris flowers data set]] is a well-known example of a classification issue. The data set includes 150 samples of iris flowers. Each sample has four features: the sepal width and length, and the length and width of the petals. The samples FNs are from one of three classes: [[Iris vericolor]]especially important in certain applications, [[Iris virginica]]such as medical diagnosis or fraud detection, and [[Iris setosa]. It where it is necessary costly to build a model from the data fail to predict the class of new Iris flowers based on their featuresidentify positive samples.

==Support Vector Machine (SVM)Examples==Support Vector Machine]] algorithm. The SVM algorithm determines the best decision boundary (also called In a medical diagnosis scenario, a FN could occur when a hyperplane) model that is supposed to separate identify patients with a specific disease incorrectly classifies a patient's health as healthy, when in fact they have the disease. This could result in the different classes within patient not receiving the feature space. The decision boundary on treatment they need, which new input objects fall determines how they are classifiedcould have severe consequences.

==Example Implementation A fraud detection scenario would see a FN if a model that is designed to detect fraudulent transactions incorrectly classifies a fraudulent deal as legitimate. This could result in Python==Here financial loss as the fraudulent transaction is an example of how one might implement the Iris classification task using the SVM algorithm in [[Python (programming language)|Python]]:approved.

# Load FNR is the iris datasetiris = datasetssum of the total number positive samples and the number of FNs.load_iris()X = iris.datay = irisIt is the percentage of positive samples incorrectly classified as being negative.target

# Create an SVM classifier with a linear kernelclf = svm.SVC(kernel='linear') # Train The FDR is the classifier on the iris datasetclf.fit(X, y) # Predict sum of the class number of a new, unknown irisnew_iris = [[5.0, 3.6, 1.3, 0.25]]predicted_class = clf.predict(new_iris) # Print FNs and the total number that were predicted classprint(predicted_class)``` This code would output to be positive. It is the class percentage of the new iris, for example 'setosa'negative samples that are incorrectly classified to be positive.

Machine learning allows computers to learn without having to be programmed. One example is False negatives are when a game in which the computer attempts to identify what kind of flower claims something is false when it is seeing based on photos of other flowers it has seenactually true. The A false negative is when a computer is shown pictures of different flowers and is told what kind of flower it is looking atclaims that a person isn't sick, but in fact they are. After looking at many examples, the computer This can see a new photo of a flower lead to determine what type it isthe person not receiving the medication they need.